| With the continuous improvement of the development level of government information,the government website has also produced more and more government data,among which the personnel information data is a very important and high research value data.On the one hand,people are the subject of each unit and department,and the information related to people is the basis of each decision,which has an important role;on the other hand,the current personnel information mainly exists in the independent text of personnel appointments and dismissals,political news,etc.,which are scattered and unrelated,which wastes the value of the information itself.The technology related to natural language processing is developing rapidly nowadays,which provides technical support for processing personnel information data of government affairs,and the application of knowledge graph also provides an intuitive and effective tool for related academic research and field development.Deep learning can extract structured triadic information from unstructured text data,on which the knowledge graph of government personnel information can be built,but there are still problems such as the lack of data sets and the lack of accuracy of existing algorithm models.To address the above issues,the main research of this paper is as follows:(1)Create a dataset of personnel information from the government website.We use Python language to write a crawler program to get the original text data from the government website and clean the data,determine the entity type and relationship type and then annotate the text data,and finally convert the text into a specific format to complete the creation of the dataset.The dataset contains a total of 10 relationship categories and corresponding entity categories,which can provide data support for subsequent experimental research and promote the development of entity relationship extraction in the field of government personnel information.(2)A joint GCN-Cas Rel entity relationship extraction model incorporating dependency syntax analysis is proposed.The model is based on the end-to-end cascaded binary tagging framework of Cas Rel model.While solving the triad overlap problem in the text of government personnel information,the model uses graph convolutional neural network to model the dependent syntactic relations so that the model can better capture the syntactic structure information,and introduces the attention mechanism to filter the noise of the dependent syntactic tree to improve the entity relation extraction performance.After the experimental comparison with other joint extraction models,the accuracy rate and F1 value of this model have been improved,which proves the effectiveness of the model.(3)Constructe a knowledge graph of personnel information of governmental websites.An entity relationship extraction applet based on the We Chat platform is developed using the proposed joint entity relationship extraction model.The applet is divided into three parts: frontend page,back-end server and data side.Users can directly obtain the triad extraction results by uploading text or URL,and on this basis,the Neo4 j graph database is used to visualize the personnel information triad extracted by the applet and complete the creation of the personnel information knowledge map of the government affairs website.In this paper,we crawl the text data of government websites,establish the personnel information dataset of government websites after preprocessing,introduce graph convolutional neural network to model the dependent syntactic relations based on the end-to-end cascaded binary tagging framework,and filter the noise of the dependent syntactic tree by using the attention mechanism,and prove the model has good effect by experiment.Based on the We Chat platform,we developed an applet for extracting entity relations of government personnel information,and used the applet to extract entity relations of some government texts in Gansu Province,and completed the knowledge graph visualization using Neo4 j graph database after obtaining the personnel triad information. |
